Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

Urushibara, Aiko; Mori, Kensaku; Ishiguro, Toshitaka; Sakai, Masafumi; Masuoka, Souta; Masumoto, T.

doi:10.1016/j.ejrad.2020.109471

Cited by 31 publications

(18 citation statements)

References 29 publications

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“…It was not surprising that the XGBoost performed better that the DNN algorithm for the prediction problem. The use of Deep Learning in not new in Gynaecological Oncology [ 61 ], with a selective preference for Gynaecological imaging, exhibiting a good diagnostic performance. The model appeared to be more precise for group 1 (SCS < 5); hence, it could potentially identify those patient groups, who, under the influence of human factors, might benefit from a less aggressive surgery to recover more rapidly, and then embark on timely adjuvant treatment.…”

Section: Discussionmentioning

confidence: 99%

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer

Laios

Kalampokis

Johnson

et al. 2022

Cancers

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(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598–0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69–0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655–0.823; p < 0.95). We identified “turning points” that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.

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Section: Discussionmentioning

confidence: 99%

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer

Laios

Kalampokis

Johnson

et al. 2022

Cancers

View full text Add to dashboard Cite

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“…Few studies have used deep learning in the field of gynecological imaging. Urushibara et al recently constructed a CNN that showed good diagnostic performance for identifying the presence of cervical cancer on T2WI [ 17 ]. Aramendía et al developed a CAD technique for US images that was able to discriminate between malignant and benign adnexal masses based on a texture analysis of 145 patients [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments

Mori

Hoshiai

Sakai

et al. 2022

Cancers

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Background: This study aimed to compare deep learning with radiologists’ assessments for diagnosing ovarian carcinoma using MRI. Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. Results: The CNN of each sequence had a sensitivity of 0.77–0.85, specificity of 0.77–0.92, accuracy of 0.81–0.87, and an AUC of 0.83–0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). Conclusion: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI.

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“…Several CNNs using MRI have been constructed to diagnose uterine tumors to date [ 20 , 21 ]. Urushibara et al recently developed a CNN that can differentiate between cervical cancer and non-cancerous lesions on T2WI [ 22 ]. Chen et al and Dong et al evaluated the myometrial infiltration of endometrial cancer using CNN and T2WI [ 23 ], and T2WI + CE-T1WI [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists

et al. 2022

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Purpose To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. Results The AUC of the CNNs pertaining to the single and combined image sets were 0.88–0.95 and 0.87–0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88–0.95. Conclusion CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.

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Diagnosing uterine cervical cancer on a single T2-weighted image: Comparison between deep learning versus radiologists

Cited by 31 publications

References 29 publications

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments

The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists

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